Catalog
concept#AI#Governance#Reliability#Security

AI Ethics

Principles and governance for the responsible design, assessment and use of AI systems.

AI ethics defines principles, policies and governance mechanisms for the responsible design and use of artificial intelligence.
Emerging
Medium

Classification

  • Medium
  • Organizational
  • Organizational
  • Intermediate

Technical context

Model governance platforms (e.g. model registry)Privacy and DLP systemsIncident management and ticketing systems

Principles & goals

Transparency of decisions and data provenanceAccountability and clear rolesMinimization of bias and discrimination
Discovery
Enterprise, Domain, Team

Use cases & scenarios

Compromises

  • Over‑specification can inhibit innovation
  • Tokenistic solutions without real bias mitigation
  • Liability and reputational risks from misdecisions
  • Early involvement of interdisciplinary stakeholders
  • Standardized testing and evaluation procedures
  • Transparent documentation of decisions

I/O & resources

  • Datasets and metadata
  • Regulatory requirements
  • Roles and responsibility matrix
  • Governance policies and audit reports
  • Model approval artifacts
  • Metrics for monitoring fairness and safety

Description

AI ethics defines principles, policies and governance mechanisms for the responsible design and use of artificial intelligence. It covers risk assessment, bias mitigation, transparency, accountability and regulatory compliance, plus organizational processes for monitoring, evaluating and continuously improving AI systems in products and decision workflows. It includes policies and audit practices.

✔

  • Reduced regulatory risk through proactive compliance
  • Increased user trust and brand reputation
  • Improved product quality via early detection of issues

✖

  • No absolute error elimination for complex models
  • Fairness measurement is context‑dependent and partly subjective
  • Implementation can be time‑ and resource‑intensive

  • Fairness index

    Quantitative measure to assess bias between relevant groups.

  • Explainability score

    Measure of model decision traceability for stakeholders.

  • Incident rate

    Number of relevant AI incidents per operation period.

Code of conduct for AI development

Company‑wide code with minimum requirements for data use, testing and transparency.

Bias report for credit decision system

Documented analysis of fairness metrics prior to rollout of a credit scoring model.

Responsible AI vendor assessment

Standardized assessment procedure to evaluate third‑party models for compliance and security.

1

Perform an inventory analysis of deployed AI systems.

2

Define governance structure and responsibilities.

3

Introduce processes for audits, reviews and escalation.

4

Establish continuous measurement and reporting.

⚠️ Technical debt & bottlenecks

  • Missing telemetry for fairness metrics
  • Ad‑hoc patches instead of sustainable fixes
  • Unversioned models and datasets
Insufficient data qualityUnclear responsibilitiesMissing fairness metrics
  • Superficial bias tests before launch
  • Releasing uninterpretable decision logic
  • Outsourcing audit duties to vendors without oversight
  • Over‑regulation prevents quick remediation
  • Confusing transparency with disclosure of sensitive data
  • Unclear metric definitions lead to wrong conclusions
Basic knowledge of statistics and fairness metricsLegal and compliance understandingModel validation and ML testing
Explainability and traceability of model outputsPrivacy and secure data handlingAuditability and demonstrability of decisions
  • • Legal requirements and privacy laws
  • • Limited resources for audits
  • • Technical constraints of existing systems